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Ligand identification using electron-density map correlations.

Terwilliger TC, Adams PD, Moriarty NW, Cohn JD - Acta Crystallogr. D Biol. Crystallogr. (2006)

Bottom Line: The two characteristics are scored using a Z-score approach in which the correlations are normalized to the mean and standard deviation of correlations found for a variety of mismatched ligand-density pairs, so that the Z scores are related to the probability of observing a particular value of the correlation by chance.The procedure was tested with a set of 200 of the most commonly found ligands in the Protein Data Bank, collectively representing 57% of all ligands in the Protein Data Bank.This approach may be useful in identification of unknown ligands in new macromolecular structures as well as in the identification of which ligands in a mixture have bound to a macromolecule.

View Article: PubMed Central - HTML - PubMed

Affiliation: Los Alamos National Laboratory, Mailstop M888, Los Alamos, NM 87545, USA. terwilliger@lanl.gov

ABSTRACT
A procedure for the identification of ligands bound in crystal structures of macromolecules is described. Two characteristics of the density corresponding to a ligand are used in the identification procedure. One is the correlation of the ligand density with each of a set of test ligands after optimization of the fit of that ligand to the density. The other is the correlation of a fingerprint of the density with the fingerprint of model density for each possible ligand. The fingerprints consist of an ordered list of correlations of each the test ligands with the density. The two characteristics are scored using a Z-score approach in which the correlations are normalized to the mean and standard deviation of correlations found for a variety of mismatched ligand-density pairs, so that the Z scores are related to the probability of observing a particular value of the correlation by chance. The procedure was tested with a set of 200 of the most commonly found ligands in the Protein Data Bank, collectively representing 57% of all ligands in the Protein Data Bank. Using a combination of these two characteristics of ligand density, ranked lists of ligand identifications were made for representative (F(o) - F(c))exp(i(phi)c) difference density from entries in the Protein Data Bank. In 48% of the 200 cases, the correct ligand was at the top of the ranked list of ligands. This approach may be useful in identification of unknown ligands in new macromolecular structures as well as in the identification of which ligands in a mixture have bound to a macromolecule.

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Fingerprints of difference density. (a) Correlation of each of 119 unique ligands after fitting to difference density for tris-(hydroxyamino)-methane from PDB entry 1m6z (A. Noergaard, P. Harris, S. Larsen & H. E. M. Christensen, unpublished work) at a resolution of 1.4 Å. The ligands are sorted from left to right based on increasing numbers of non-H atoms. (b) As in (a), except fitting to difference density for ATP from PDB entry 1aq2 at a resolution of 1.9 Å (Tari et al., 1997 ▶). The correlations are all indicated by filled triangles, except for the correlation of the correct ligand, which is indicated by an open diamond.
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fig5: Fingerprints of difference density. (a) Correlation of each of 119 unique ligands after fitting to difference density for tris-(hydroxyamino)-methane from PDB entry 1m6z (A. Noergaard, P. Harris, S. Larsen & H. E. M. Christensen, unpublished work) at a resolution of 1.4 Å. The ligands are sorted from left to right based on increasing numbers of non-H atoms. (b) As in (a), except fitting to difference density for ATP from PDB entry 1aq2 at a resolution of 1.9 Å (Tari et al., 1997 ▶). The correlations are all indicated by filled triangles, except for the correlation of the correct ligand, which is indicated by an open diamond.

Mentions: The process of fitting each of 119 ligands to difference density and obtaining correlation coefficients for each fit yields some information that we have not taken full advantage of by simply choosing the highest correlation or Z score to identify the best-fitting ligand. This additional information is the pattern of fits of the entire set of 119 ligands. Fig. 5 ▶ illustrates the fingerprints for difference density for tris-(hydroxylmethyl)-methane and for ATP. The correlation coefficients for each of the 119 ligands are shown, where the ligands are sorted on the basis of the number of non-H atoms. The fingerprint for tris-(hydroxylmethyl)-methane shows that many small ligands fit well to its difference density, while large ligands do not. In the case of ATP, the pattern is much more complicated, with some small and some large ligands fitting well and others not.


Ligand identification using electron-density map correlations.

Terwilliger TC, Adams PD, Moriarty NW, Cohn JD - Acta Crystallogr. D Biol. Crystallogr. (2006)

Fingerprints of difference density. (a) Correlation of each of 119 unique ligands after fitting to difference density for tris-(hydroxyamino)-methane from PDB entry 1m6z (A. Noergaard, P. Harris, S. Larsen & H. E. M. Christensen, unpublished work) at a resolution of 1.4 Å. The ligands are sorted from left to right based on increasing numbers of non-H atoms. (b) As in (a), except fitting to difference density for ATP from PDB entry 1aq2 at a resolution of 1.9 Å (Tari et al., 1997 ▶). The correlations are all indicated by filled triangles, except for the correlation of the correct ligand, which is indicated by an open diamond.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2483487&req=5

fig5: Fingerprints of difference density. (a) Correlation of each of 119 unique ligands after fitting to difference density for tris-(hydroxyamino)-methane from PDB entry 1m6z (A. Noergaard, P. Harris, S. Larsen & H. E. M. Christensen, unpublished work) at a resolution of 1.4 Å. The ligands are sorted from left to right based on increasing numbers of non-H atoms. (b) As in (a), except fitting to difference density for ATP from PDB entry 1aq2 at a resolution of 1.9 Å (Tari et al., 1997 ▶). The correlations are all indicated by filled triangles, except for the correlation of the correct ligand, which is indicated by an open diamond.
Mentions: The process of fitting each of 119 ligands to difference density and obtaining correlation coefficients for each fit yields some information that we have not taken full advantage of by simply choosing the highest correlation or Z score to identify the best-fitting ligand. This additional information is the pattern of fits of the entire set of 119 ligands. Fig. 5 ▶ illustrates the fingerprints for difference density for tris-(hydroxylmethyl)-methane and for ATP. The correlation coefficients for each of the 119 ligands are shown, where the ligands are sorted on the basis of the number of non-H atoms. The fingerprint for tris-(hydroxylmethyl)-methane shows that many small ligands fit well to its difference density, while large ligands do not. In the case of ATP, the pattern is much more complicated, with some small and some large ligands fitting well and others not.

Bottom Line: The two characteristics are scored using a Z-score approach in which the correlations are normalized to the mean and standard deviation of correlations found for a variety of mismatched ligand-density pairs, so that the Z scores are related to the probability of observing a particular value of the correlation by chance.The procedure was tested with a set of 200 of the most commonly found ligands in the Protein Data Bank, collectively representing 57% of all ligands in the Protein Data Bank.This approach may be useful in identification of unknown ligands in new macromolecular structures as well as in the identification of which ligands in a mixture have bound to a macromolecule.

View Article: PubMed Central - HTML - PubMed

Affiliation: Los Alamos National Laboratory, Mailstop M888, Los Alamos, NM 87545, USA. terwilliger@lanl.gov

ABSTRACT
A procedure for the identification of ligands bound in crystal structures of macromolecules is described. Two characteristics of the density corresponding to a ligand are used in the identification procedure. One is the correlation of the ligand density with each of a set of test ligands after optimization of the fit of that ligand to the density. The other is the correlation of a fingerprint of the density with the fingerprint of model density for each possible ligand. The fingerprints consist of an ordered list of correlations of each the test ligands with the density. The two characteristics are scored using a Z-score approach in which the correlations are normalized to the mean and standard deviation of correlations found for a variety of mismatched ligand-density pairs, so that the Z scores are related to the probability of observing a particular value of the correlation by chance. The procedure was tested with a set of 200 of the most commonly found ligands in the Protein Data Bank, collectively representing 57% of all ligands in the Protein Data Bank. Using a combination of these two characteristics of ligand density, ranked lists of ligand identifications were made for representative (F(o) - F(c))exp(i(phi)c) difference density from entries in the Protein Data Bank. In 48% of the 200 cases, the correct ligand was at the top of the ranked list of ligands. This approach may be useful in identification of unknown ligands in new macromolecular structures as well as in the identification of which ligands in a mixture have bound to a macromolecule.

Show MeSH
Related in: MedlinePlus